{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "sns.set()\n",
    "tf.compat.v1.random.set_random_seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>close</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-08-09T23:00:00</td>\n",
       "      <td>11860.074544</td>\n",
       "      <td>0.672896</td>\n",
       "      <td>0.327104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-08-09T23:20:00</td>\n",
       "      <td>11872.025879</td>\n",
       "      <td>0.595100</td>\n",
       "      <td>0.404900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-08-09T23:40:00</td>\n",
       "      <td>11880.504557</td>\n",
       "      <td>0.596702</td>\n",
       "      <td>0.403298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-08-10T00:00:00</td>\n",
       "      <td>11918.873481</td>\n",
       "      <td>0.577972</td>\n",
       "      <td>0.422028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-08-10T00:20:00</td>\n",
       "      <td>11937.581272</td>\n",
       "      <td>0.585342</td>\n",
       "      <td>0.414658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp         close  positive  negative\n",
       "0  2019-08-09T23:00:00  11860.074544  0.672896  0.327104\n",
       "1  2019-08-09T23:20:00  11872.025879  0.595100  0.404900\n",
       "2  2019-08-09T23:40:00  11880.504557  0.596702  0.403298\n",
       "3  2019-08-10T00:00:00  11918.873481  0.577972  0.422028\n",
       "4  2019-08-10T00:20:00  11937.581272  0.585342  0.414658"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../dataset/BTC-sentiment.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How we gather the data, provided by Bitcurate, bitcurate.com\n",
    "\n",
    "Because I don't have sentiment data related to stock market, so I will use crpytocurrency data, `BTC/USDT` from binance.\n",
    "\n",
    "1. close data came from CCXT, https://github.com/ccxt/ccxt, an open source cryptocurrency aggregator.\n",
    "2. We gather from streaming twitter, crawling hardcoded crpyocurrency telegram groups and Reddit. And we store in Elasticsearch as a single index. We trained 1/4 layers BERT MULTILANGUAGE (200MB-ish, originally 700MB-ish) released by Google on most-possible-found sentiment data on the internet, leveraging sentiment on multilanguages, eg, english, korea, japan. **Actually, it is very hard to found negative sentiment related to bitcoin / btc in large volume.**\n",
    "\n",
    "How we request using elasticsearch-dsl, https://elasticsearch-dsl.readthedocs.io,\n",
    "```python\n",
    "# from index name\n",
    "s = s.filter(\n",
    "    'query_string',\n",
    "    default_field = 'text',\n",
    "    query = 'bitcoin OR btc',\n",
    ")\n",
    "```\n",
    "\n",
    "We only do text query only contain `bitcoin` or `btc`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consensus introduction\n",
    "\n",
    "We have 2 questions here when saying about consensus, what happened,\n",
    "\n",
    "1. to future price if we assumed future sentiment is really positive, near to 1.0 . Eg, suddenly China want to adapt cryptocurrency and that can cause huge requested volumes.\n",
    "2. to future price if we assumed future sentiment is really negative, near to 1.0 . Eg, suddenly hackers broke binance or any exchanges, or any news that caused wreck by negative sentiment.\n",
    "\n",
    "**We can use deep-learning to simulate for us!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1 import host_subplot\n",
    "import mpl_toolkits.axisartist as AA\n",
    "\n",
    "close = df['close'].tolist()\n",
    "positive = df['positive'].tolist()\n",
    "negative = df['negative'].tolist()\n",
    "timestamp = df['timestamp'].tolist()\n",
    "\n",
    "plt.figure(figsize = (17, 5))\n",
    "host = host_subplot(111)\n",
    "plt.subplots_adjust(right = 0.75, top = 0.8)\n",
    "par1 = host.twinx()\n",
    "par2 = host.twinx()\n",
    "\n",
    "par2.spines['right'].set_position(('axes', 1.1))\n",
    "par2.spines['bottom'].set_position(('axes', 0.9))\n",
    "host.set_xlabel('timestamp')\n",
    "host.set_ylabel('BTC/USDT')\n",
    "par1.set_ylabel('positive')\n",
    "par2.set_ylabel('negative')\n",
    "\n",
    "host.plot(close, label = 'BTC/USDT')\n",
    "par1.plot(positive, label = 'positive')\n",
    "par2.plot(negative, label = 'negative')\n",
    "host.legend()\n",
    "plt.xticks(\n",
    "        np.arange(len(timestamp))[::30], timestamp[::30], rotation = '45', ha = 'right'\n",
    "    )\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.947020</td>\n",
       "      <td>0.672896</td>\n",
       "      <td>0.327104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.955190</td>\n",
       "      <td>0.595100</td>\n",
       "      <td>0.404900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.960986</td>\n",
       "      <td>0.596702</td>\n",
       "      <td>0.403298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.987212</td>\n",
       "      <td>0.577972</td>\n",
       "      <td>0.422028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.585342</td>\n",
       "      <td>0.414658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.947020  0.672896  0.327104\n",
       "1  0.955190  0.595100  0.404900\n",
       "2  0.960986  0.596702  0.403298\n",
       "3  0.987212  0.577972  0.422028\n",
       "4  1.000000  0.585342  0.414658"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmax = MinMaxScaler().fit(df.iloc[:, 1:2].astype('float32'))\n",
    "df_log = minmax.transform(df.iloc[:, 1:2].astype('float32'))\n",
    "df_log = pd.DataFrame(df_log)\n",
    "df_log[1] = df['positive']\n",
    "df_log[2] = df['negative']\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model definition\n",
    "\n",
    "This example is using model 17.cnn-seq2seq, if you want to use another model, need to tweak a little bit, but I believe it is not that hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((339, 4), (309, 3), (30, 3))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_layers = 1\n",
    "size_layer = 128\n",
    "epoch = 200\n",
    "dropout_rate = 0.75\n",
    "test_size = 3 * 10 # timestamp every 20 minutes, and I want to test on last 12 hours\n",
    "learning_rate = 1e-3\n",
    "timestamp = test_size\n",
    "\n",
    "df_train = df_log.iloc[:-test_size]\n",
    "df_test = df_log.iloc[-test_size:]\n",
    "df.shape, df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encoder_block(inp, n_hidden, filter_size):\n",
    "    inp = tf.expand_dims(inp, 2)\n",
    "    inp = tf.pad(\n",
    "        inp,\n",
    "        [\n",
    "            [0, 0],\n",
    "            [(filter_size[0] - 1) // 2, (filter_size[0] - 1) // 2],\n",
    "            [0, 0],\n",
    "            [0, 0],\n",
    "        ],\n",
    "    )\n",
    "    conv = tf.layers.conv2d(\n",
    "        inp, n_hidden, filter_size, padding = 'VALID', activation = None\n",
    "    )\n",
    "    conv = tf.squeeze(conv, 2)\n",
    "    return conv\n",
    "\n",
    "\n",
    "def decoder_block(inp, n_hidden, filter_size):\n",
    "    inp = tf.expand_dims(inp, 2)\n",
    "    inp = tf.pad(inp, [[0, 0], [filter_size[0] - 1, 0], [0, 0], [0, 0]])\n",
    "    conv = tf.layers.conv2d(\n",
    "        inp, n_hidden, filter_size, padding = 'VALID', activation = None\n",
    "    )\n",
    "    conv = tf.squeeze(conv, 2)\n",
    "    return conv\n",
    "\n",
    "\n",
    "def glu(x):\n",
    "    return tf.multiply(\n",
    "        x[:, :, : tf.shape(x)[2] // 2],\n",
    "        tf.sigmoid(x[:, :, tf.shape(x)[2] // 2 :]),\n",
    "    )\n",
    "\n",
    "\n",
    "def layer(inp, conv_block, kernel_width, n_hidden, residual = None):\n",
    "    z = conv_block(inp, n_hidden, (kernel_width, 1))\n",
    "    return glu(z) + (residual if residual is not None else 0)\n",
    "\n",
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        learning_rate,\n",
    "        num_layers,\n",
    "        size,\n",
    "        size_layer,\n",
    "        output_size,\n",
    "        kernel_size = 3,\n",
    "        n_attn_heads = 16,\n",
    "        dropout = 0.9,\n",
    "    ):\n",
    "        self.X = tf.placeholder(tf.float32, (None, None, size))\n",
    "        self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
    "\n",
    "        encoder_embedded = tf.layers.dense(self.X, size_layer)\n",
    "\n",
    "        e = tf.identity(encoder_embedded)\n",
    "        for i in range(num_layers):\n",
    "            z = layer(\n",
    "                encoder_embedded,\n",
    "                encoder_block,\n",
    "                kernel_size,\n",
    "                size_layer * 2,\n",
    "                encoder_embedded,\n",
    "            )\n",
    "            z = tf.nn.dropout(z, keep_prob = dropout)\n",
    "            encoder_embedded = z\n",
    "\n",
    "        encoder_output, output_memory = z, z + e\n",
    "        g = tf.identity(encoder_embedded)\n",
    "\n",
    "        for i in range(num_layers):\n",
    "            attn_res = h = layer(\n",
    "                encoder_embedded,\n",
    "                decoder_block,\n",
    "                kernel_size,\n",
    "                size_layer * 2,\n",
    "                residual = tf.zeros_like(encoder_embedded),\n",
    "            )\n",
    "            C = []\n",
    "            for j in range(n_attn_heads):\n",
    "                h_ = tf.layers.dense(h, size_layer // n_attn_heads)\n",
    "                g_ = tf.layers.dense(g, size_layer // n_attn_heads)\n",
    "                zu_ = tf.layers.dense(\n",
    "                    encoder_output, size_layer // n_attn_heads\n",
    "                )\n",
    "                ze_ = tf.layers.dense(output_memory, size_layer // n_attn_heads)\n",
    "\n",
    "                d = tf.layers.dense(h_, size_layer // n_attn_heads) + g_\n",
    "                dz = tf.matmul(d, tf.transpose(zu_, [0, 2, 1]))\n",
    "                a = tf.nn.softmax(dz)\n",
    "                c_ = tf.matmul(a, ze_)\n",
    "                C.append(c_)\n",
    "\n",
    "            c = tf.concat(C, 2)\n",
    "            h = tf.layers.dense(attn_res + c, size_layer)\n",
    "            h = tf.nn.dropout(h, keep_prob = dropout)\n",
    "            encoder_embedded = h\n",
    "\n",
    "        encoder_embedded = tf.sigmoid(encoder_embedded[-1])\n",
    "        self.logits = tf.layers.dense(encoder_embedded, output_size)\n",
    "        self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))\n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(\n",
    "            self.cost\n",
    "        )\n",
    "        \n",
    "def calculate_accuracy(real, predict):\n",
    "    real = np.array(real) + 1\n",
    "    predict = np.array(predict) + 1\n",
    "    percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real)))\n",
    "    return percentage * 100\n",
    "\n",
    "def anchor(signal, weight):\n",
    "    buffer = []\n",
    "    last = signal[0]\n",
    "    for i in signal:\n",
    "        smoothed_val = last * weight + (1 - weight) * i\n",
    "        buffer.append(smoothed_val)\n",
    "        last = smoothed_val\n",
    "    return buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0818 16:34:24.824237 140007582447424 deprecation.py:323] From <ipython-input-6-6c0655f4345e>:55: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "W0818 16:34:24.831443 140007582447424 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W0818 16:34:25.094202 140007582447424 deprecation.py:323] From <ipython-input-6-6c0655f4345e>:13: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.keras.layers.Conv2D` instead.\n",
      "W0818 16:34:25.236837 140007582447424 deprecation.py:506] From <ipython-input-6-6c0655f4345e>:66: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "modelnn = Model(\n",
    "    learning_rate, num_layers, df_log.shape[1], size_layer, df_log.shape[1], \n",
    "    dropout = dropout_rate\n",
    ")\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 200/200 [00:40<00:00,  5.17it/s, acc=98, cost=0.000637]  \n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "pbar = tqdm(range(epoch), desc = 'train loop')\n",
    "for i in pbar:\n",
    "    init_value = np.zeros((1, num_layers * 2 * size_layer))\n",
    "    total_loss, total_acc = [], []\n",
    "    for k in range(0, df_train.shape[0] - 1, timestamp):\n",
    "        index = min(k + timestamp, df_train.shape[0] - 1)\n",
    "        batch_x = np.expand_dims(\n",
    "            df_train.iloc[k : index, :].values, axis = 0\n",
    "        )\n",
    "        batch_y = df_train.iloc[k + 1 : index + 1, :].values\n",
    "        logits, _, loss = sess.run(\n",
    "            [modelnn.logits, modelnn.optimizer, modelnn.cost],\n",
    "            feed_dict = {modelnn.X: batch_x, modelnn.Y: batch_y},\n",
    "        )        \n",
    "        total_loss.append(loss)\n",
    "        total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0]))\n",
    "    pbar.set_postfix(cost = np.mean(total_loss), acc = np.mean(total_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_day = test_size\n",
    "\n",
    "output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1]))\n",
    "output_predict[0] = df_train.iloc[0]\n",
    "upper_b = (df_train.shape[0] // timestamp) * timestamp\n",
    "\n",
    "for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp):\n",
    "    out_logits = sess.run(\n",
    "        modelnn.logits,\n",
    "        feed_dict = {\n",
    "            modelnn.X: np.expand_dims(\n",
    "                df_train.iloc[k : k + timestamp], axis = 0\n",
    "            )\n",
    "        },\n",
    "    )\n",
    "    output_predict[k + 1 : k + timestamp + 1] = out_logits\n",
    "\n",
    "if upper_b != df_train.shape[0]:\n",
    "    out_logits = sess.run(\n",
    "        modelnn.logits,\n",
    "        feed_dict = {\n",
    "            modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0)\n",
    "        },\n",
    "    )\n",
    "    output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits\n",
    "    future_day -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_predict_negative = output_predict.copy()\n",
    "output_predict_positive = output_predict.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(future_day):\n",
    "    o = output_predict[-future_day - timestamp + i:-future_day + i].copy()\n",
    "    o = np.expand_dims(o, axis = 0)\n",
    "    \n",
    "    o_negative = output_predict_negative[-future_day - timestamp + i:-future_day + i].copy()\n",
    "    o_negative = np.expand_dims(o_negative, axis = 0)\n",
    "    o_negative[:, :, 1] = 0.0\n",
    "    o_negative[:, :, 2] = 1.0\n",
    "    \n",
    "    o_positive = output_predict_positive[-future_day - timestamp + i:-future_day + i].copy()\n",
    "    o_positive = np.expand_dims(o_positive, axis = 0)\n",
    "    o_positive[:, :, 1] = 1.0\n",
    "    o_positive[:, :, 2] = 0.0\n",
    "    \n",
    "    # original without any consensus\n",
    "    out_logits = sess.run(\n",
    "        modelnn.logits,\n",
    "        feed_dict = {\n",
    "            modelnn.X: o\n",
    "        },\n",
    "    )\n",
    "    output_predict[-future_day + i] = out_logits[-1]\n",
    "    \n",
    "    # negative consensus\n",
    "    out_logits = sess.run(\n",
    "        modelnn.logits,\n",
    "        feed_dict = {\n",
    "            modelnn.X: o_negative\n",
    "        },\n",
    "    )\n",
    "    output_predict_negative[-future_day + i] = out_logits[-1]\n",
    "    \n",
    "    # positive consensus\n",
    "    out_logits = sess.run(\n",
    "        modelnn.logits,\n",
    "        feed_dict = {\n",
    "            modelnn.X: o_positive\n",
    "        },\n",
    "    )\n",
    "    output_predict_positive[-future_day + i] = out_logits[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_predict_original = minmax.inverse_transform(output_predict[:,:1])\n",
    "output_predict_negative = minmax.inverse_transform(output_predict_negative[:,:1])\n",
    "output_predict_positive = minmax.inverse_transform(output_predict_positive[:,:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_future = anchor(output_predict_original[:, 0], 0.7)\n",
    "deep_future_negative = anchor(output_predict_negative[:, 0], 0.7)\n",
    "deep_future_positive = anchor(output_predict_positive[:, 0], 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((339, 4), 339)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape, len(deep_future_negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = minmax.inverse_transform(df_train)\n",
    "df_test = minmax.inverse_transform(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timestamp = df['timestamp'].tolist()\n",
    "pad_test = np.pad(df_test[:,0], (df_train.shape[0], 0), 'constant', constant_values=np.nan)\n",
    "\n",
    "plt.figure(figsize = (15, 5))\n",
    "plt.plot(pad_test, label = 'test trend', c = 'blue')\n",
    "plt.plot(df_train[:,0], label = 'train trend', c = 'black')\n",
    "plt.plot(deep_future, label = 'forecast without consensus')\n",
    "plt.plot(deep_future_negative, label = 'forecast with negative consensus', c = 'red')\n",
    "plt.plot(deep_future_positive, label = 'forecast with positive consensus', c = 'green')\n",
    "plt.legend()\n",
    "plt.xticks(\n",
    "    np.arange(len(timestamp))[::30], timestamp[::30], rotation = '45', ha = 'right'\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What we can observe\n",
    "\n",
    "1. The model learn, if positive and negative sentiments increasing, both will increase the price. That is why, using positive consensus or negative consensus caused price going up.\n",
    "2. Volatility of price is higher if negative sentiment is higher, still positive volatility.\n",
    "3. Momentum of price is higher if negative sentiment is higher, still positive momentum."
   ]
  },
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   "outputs": [],
   "source": []
  }
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